The effect of prey abundance and fisheries on the survival, reproduction, and social structure of killer whales (Orcinus orca) at subantarctic Marion Island

Abstract Most marine apex predators are keystone species that fundamentally influence their ecosystems through cascading top‐down processes. Reductions in worldwide predator abundances, attributed to environmental‐ and anthropogenic‐induced changes to prey availability and negative interactions with fisheries, can have far‐reaching ecosystem impacts. We tested whether the survival of killer whales (Orcinus orca) observed at Marion Island in the Southern Indian Ocean correlated with social structure and prey variables (direct measures of prey abundance, Patagonian toothfish fishery effort, and environmental proxies) using multistate models of capture–recapture data spanning 12 years (2006–2018). We also tested the effect of these same variables on killer whale social structure and reproduction measured over the same period. Indices of social structure had the strongest correlation with survival, with higher sociality associated with increased survival probability. Survival was also positively correlated with Patagonian toothfish fishing effort during the previous year, suggesting that fishery‐linked resource availability is an important determinant of survival. No correlation between survival and environmental proxies of prey abundance was found. At‐island prey availability influenced the social structure of Marion Island killer whales, but none of the variables explained variability in reproduction. Future increases in legal fishing activity may benefit this population of killer whales through the artificial provisioning of resources they provide.


| INTRODUC TI ON
Top predators strongly influence the structure and function of marine ecosystems (Estes et al., 2016;Estes & Duggins, 1995;Paine, 1980). However, due to environmental and anthropogenic changes, the global abundance of upper trophic-level marine predators has declined (Estes et al., 2011;Hutchings & Baum, 2005).
Many of these predators are keystone species, and declines in their abundance can trigger trophic cascades and downgrading of ecosystems (Estes et al., 2011;Pace et al., 1999). For example, diet switching by killer whales Orcinus orca in the Aleutian archipelago reduced sea otter Enhydra lutris population sizes, thereby releasing sea urchins from top-down control and initiating a trophic cascade that transformed the kelp forest ecosystem (Estes et al., 1998).
Given that marine predator declines (or in some cases population increases; Kitchell et al., 2006) can have far-reaching ecosystem impacts through cascading top-down processes, it is important to understand what environmental and anthropogenic factors regulate their population dynamics (Baum & Worm, 2009;Heithaus et al., 2008).
Bottom-up (resource limitation) and top-down (predation) mechanisms often act together to regulate the growth of animal populations (Leaper et al., 2006). Changes in resource availability due to environmental change are a major threat to the demographic resilience of many marine predators. If environmental change reduces prey populations, bottom-up regulation of predator populations is likely to follow through changes in demographic parameters, including survival and reproductive rates (van den Hoff et al., 2014). Such environmentally driven population declines have occurred in many seal and seabird species inhabiting in the Southern Ocean (Weimerskirch et al., 2003). Human activities such as fisheries can exacerbate environmentally driven food limitation. African penguins Spheniscus demersus, for example, struggle to cope with recent shifts in the distribution of southern Benguela sardine Sardinops sagax and increased competition for food with purse-seine fisheries, which leads to substantial adult mortality (Crawford, 1998;Sherley et al., 2014). Additionally, resource availability is linked to reproductive rates, with suppressed reproductive rates typical during periods of low prey availability (White & Ralls, 1993). In contrast, availability of anthropogenic food sources may increase reproductive output. For example, black bears Ursus americanus in urban areas with access to greater food availability have higher reproductive rates compared with bears in natural areas with lower food availability (Beckman & Lackey, 2008). Behavioral responses, including the use of anthropogenic resources and changes in social structure in social species, may thus allow predators to mitigate changes in prey abundances in some cases (Jordaan et al., 2021;Whitehead & Kahn, 1992).
The potential for conflict between marine predators and fisheries extends beyond prey depletion and competition for prey.
Predators are, in fact, often attracted to fisheries by the foraging opportunities they provide, and some predators may benefit from interacting with fisheries (Barbraud et al., 2012). Typically, predators either take fish that have been caught in nets or by hooks (depredation) or they target escaped or discarded fish (Söffker et al., 2015;Tixier et al., 2020). For example, killer whales that depredate legal fisheries show increased survival and reproduction rates when compared to nondepredating individuals in the same population (Esteban et al., 2016;Tixier et al., 2015Tixier et al., , 2017. But, many marine predators suffer from increased mortality due to direct interactions with fisheries (Carretta et al., 2019;Heithaus et al., 2008). These mortalities arise from animals being caught or entangled in fishing gear, or via retaliation from fishers that sometimes make use of firearms or explosives to repel predators (Jepsen & de Bruyn, 2019;Lewison et al., 2004).
Marine predator-fishery interactions that increase the mortality rates of predators may have major consequences for their population dynamics, especially when adults suffer increased mortality (Lebreton & Clobert, 1991). Additionally, fishery-related mortalities may have knock-on effects: Break up of pair-bonds in wandering albatross Diomedea exulans reduces breeding success (Mills & Ryan, 2005) and disruption of the social structure of highly social top predators such as killer whales leads to prolonged demographic stress (Busson et al., 2019). Therefore, understanding fisherypredator interactions, and the possible positive or negative effects on survival, reproduction, and social structure that arise from these interactions, is important.
Killer whales are long-lived marine predators that occupy every ocean (Ford, 2009). As apex predators, they fulfill an important role in functioning ecosystems by regulating mesopredator populations (Estes et al., 1998). Killer whales, themselves, may be regulated by prey availability. Increases in natural prey (Chinook salmon Oncorhynchus tshawytscha), for example, are associated with increases in the survival of killer whales in the Eastern North Pacific (ENP; Ford et al., 2010). Furthermore, increases in fishery-linked resource abundances (an "artificial" prey source) positively impact the survival of killer whales depredating Atlantic bluefin tuna Thunnus thynnus fisheries in the Strait of Gibraltar (Esteban et al., 2016) and legal longline Patagonian toothfish Dissostichus eleginoides fisheries in the Southern Indian Ocean (Tixier et al., 2017). In contrast, positive benefits of depredation are not associated with illegal fisheries; killer whales depredating illegal fishing vessels in the Southern Indian Ocean show decreased survival rates compared with nondepredating individuals Poncelet et al., 2010). These mortalities are known to have knock-on effects that reduce the survival of remaining killer whales due to disruptions in their social structure (Busson et al., 2019).
Here, we investigate the behavioral and demographic responses of killer whales to environmental variation. Our analysis assessed (1) the relationship between survival and environmental, prey abundance, fisheries and social structure covariates, and (2) the relationship between social structure and reproduction, environmental, prey abundance, and fishery covariates. Our analysis is based on observation data obtained from an intensive long-term, uninterrupted, photo-identification study (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) at Marion Island in the Southern Indian Ocean. Specifically, we modeled the responses of killer whale survival to changes (immediate and lagged) in natural prey availability at Marion Island, and offshore estimates of both natural and "artificial" prey availability, including measures of Patagonian toothfish fishery effort. The response of social network measures and calving rate were modeled to the same prey and fishery variables. Social structure correlates with seasonal changes in prey abundances in this population (Jordaan et al., 2021), but whether killer whale survival correlates with social structure and/or longer-term (interannual) variation in prey abundance is not known. In this context, we examine possible covariation between survival, prey abundance, and social structure. We predict that higher prey abundance and greater sociality (measured on the dyad and network level) will correlate with increased survival (Ford et al., 2010;Foster et al., 2012). As well as elucidating the response of killer whale survival to measures of prey availability, our results provide novel insight into how prey availability impacts the social structure and calving rate of this population of killer whales. In long-lived species such as killer whales, social structure and calving rate are likely to exhibit more temporal variability than adult survival; these variables may therefore better reflect demographic responses to environmental variation compared with adult survival rates (Clements et al., 2022;Reid et al., 2005). Cumulatively, our results provide insight into the effects that changes in fisheries, environmental conditions, and social structure have on the behavior and demography of the apex predator in the Southern Indian Ocean.

| Study site
Marion Island (296 km 2 ) and Prince Edward Island (45 km 2 ) lie approximately 1800 km southeast of South Africa in the Southern Indian Ocean (46°54′ S, 37°45′ E). The two islands together form the Prince Edward Islands archipelago, an important breeding site for large populations of seals and seabirds (Ryan & Bester, 2008).
Killer whales at Marion Island feed on a range of species including southern elephant seals Mirounga leonina, subantarctic fur seals Arctocephalus tropicalis, various penguins, Patagonian toothfish Dissostichus eleginoides, and possibly cephalopods (Reisinger et al., 2016;Reisinger, de Bruyn, Tosh, et al., 2011). The abundance of killer whales at the archipelago peaks during periods when inshore prey availability is high (predominantly during the elephant seal and penguin breeding seasons; Reisinger, de Bruyn, Tosh, et al., 2011). When not at the archipelago, these killer whales probably prey on Patagonian toothfish, potentially leading to interactions with commercial fishing vessels targeting the same species (Reisinger et al., 2015(Reisinger et al., , 2016. Killer whale depredation of fish caught by long lines occurs in this area (Tixier et al., , 2017Williams et al., 2009), and some individuals that are part of the Marion Island population have been photographed from fishing vessels (Tixier et al., 2021).

| Data collection and processing
Killer whale identification photographs were collected from shore at Marion Island from May 2006 to April 2018. Using various digital cameras and lenses, photographs were taken when killer whales were sighted by observers while doing other fieldwork (i.e., opportunistic sightings) or by observers conducting dedicated observation sessions. Dedicated observation sessions were conducted by trained observers who completed sessions of varying lengths (2-10 h) throughout the year at several locations of the island coastline most frequented by killer whales (Keith et al., 2001;Reisinger et al., 2015). During dedicated sessions, observers would remain at the same location and visually search for killer whales for the full, predetermined, session time. During all sightings, observers attempted to photograph the dorsal fin of each individual in the group and record the size of the group, its movement direction, and age/ sex composition. Photographing continued until the group was out of photographic range.
Through careful examination of nicks, notches, and scratches on dorsal fins and saddle patches as well as the shape and form of dorsal fins, saddle patches, and eye patches (Bigg et al., 1987), individual killer whales were identified and matched to individuals in identification catalogs (Jordaan et al., 2019;Reisinger & de Bruyn, 2014).
A quality score (ranging from 1 to 5 [unusable to excellent]) was assigned to all photographs. This score was based on the quality of lighting, focus and exposure and the size and level of obscurity of the dorsal fin in the photograph. Only sightings of individuals obtained from photographs with a quality score ≥ 3 were considered for analyses . Additionally, we excluded individuals (n = 15) that were seen less than four times during the study period in order to strengthen network analyses (Tosh et al., 2008).

| Direct measures of prey availability
Killer whale occurrence at Marion Island increases during seal (southern elephant seal and subantarctic fur seal) and penguin (king Aptenodytes patagonicus and macaroni penguin Eudyptes chrysolophus) breeding periods. Killer whales at Marion Island mostly prey on these four species when hunting inshore (Reisinger et al., 2016;Reisinger, de Bruyn, Tosh, et al., 2011), and thus, we predict that an increase in prey abundance would improve killer whale survival.
This prediction assumes prey limitation and bottom-up control of killer whale survival. We fitted annual counts of southern elephant seal pups (SES), subantarctic fur seal pups (FS), and king (KP) and macaroni (MP) penguin chicks as proxies of prey availability at Marion Island ( Figure A1; Table A1). Island-wide elephant seal pup counts were done on 15 October every year (Pistorius et al., 2011 Tixier et al., 2020). Nevertheless, given that fishing vessels facilitate access to prey for killer whales through depredation, we predict that higher fishing effort by legal fisheries would correlate with increased killer whale survival. Yearly (May-April) fishery catch and effort data (CCAMLR, 2018) were collated, and four measures of fishing effort were fitted as covariates: the number of hooks set (TF), overall catch in tons (TFc), tons of catch per 10,000 hooks (TPHK), and the tons of catch per haul (TPHL). Fishery and direct measures of prey availability covariates were fitted with zero-to 3-year time lags (t 0 , t −1 , t −2 and t −3 ). Time lags were used to consider delayed impacts of prey availability on survival . Fishery data from subarea 58.6 (Îles Crozet) were not considered as only a small number of Marion Island killer whales have been seen in this area and movement between the two areas is not known to be frequent.

| Indirect proxies of prey availability
We used the Southern Oscillation Index (SOI), Southern Annular Mode (SAM), and sea surface temperature anomalies (SSTa) as indirect proxies of prey availability. SOI is an index of El Niño-Southern Oscillation events, which result in changes in SSTa (Rasmusson & Wallace, 1983). SOI and SSTa provide indices of climatic and oceanographic variability over a small (SSTa) and large (SOI) scale and are closely associated with changes in marine food webs (Comiso et al., 1993;Croxall et al., 2002). SAM reflects extra-tropical atmospheric variability in the Southern Hemisphere and, when positive, indicates a poleward shift in westerly winds that drive circulation of the Southern Ocean currents (Thompson & Wallace, 2000). Yearly (May to April) measures of these conditions were obtained. SST data were obtained for the geographical area frequented by killer whales when not at Marion Island, as determined from previous tracking data (Reisinger et al., 2015). This area (35-50° S; 30-44° E) is in South Africa's exclusive economic zone (EEZ) and within the CCAMLR Convention subarea 58.7 (CCAMLR, 2018). Averaged values of SOI (Commonwealth of Australia, 2020) were used with a 3-and 4-year time lag as this is the time taken for oceanographic anomalies to form in this portion of the Southern Indian Ocean (Barbraud et al., 2008). SSTa (NOAA Physical Sciences Laboratory, 2020) values were calculated by subtracting the 5-year running mean from the actual measured value for each month and then averaging across months. SAM was fitted with 0-2-year time lags (t 0 , t −1 and t −2 ) to allow integration into the food web and intensification of the eddy field (Meredith & Hogg, 2006).

| Measures of social structure
Disruptions to killer whale social structure, for example, through mortalities arising from lethal responses by illegal fishers against depredating killer whales, reduce the survival probability of the remaining, closely associating, individuals (Busson et al., 2019). We therefore predict that higher social connectedness will correlate positively with the survival of Marion Island killer whales. Yearly (May to April) measures of social structure were calculated for each individual in the population and fitted in survival analysis as individual covariates. We considered the mean half-weight index (HWI; an estimate of the proportion of time that two individuals spend together; Cairns & Schwager, 1987), the degree (DEGREE; the number of associations made), and mean centrality coefficient (CC; a measure of the broadness of the network; Beauchamp, 1965) as social structure covariates. These covariates were calculated in R 4.01 (R Core Team, 2020) with the use of the "asnipe" (Farine, 2019) and "igraph" (Csardi & Nepusz, 2006) packages (see Jordaan et al., 2021).

| Measures of prey abundance
Prey abundance and the presence and scale of fisheries may impact killer whale social structure (Foster et al., 2012;Jordaan et al., 2021) and reproduction . We therefore assessed the temporal response of killer whale social network measures and reproduction to covariates of prey abundance and Patagonian toothfish fisheries. Prey abundance was represented by direct counts of prey availability at Marion Island (SES, FS, KP and MP) and indirect covariates of prey availability for at-sea areas frequently visited by Marion Island killer whales (SOI, SAM, SSTa). Pup and chick counts are used as a proxy for the total population size of prey items (SES, FS, KP, and MP). Patagonian toothfish fishing catch and effort data (TF, TFc, TPHK, and TPHL) were used as covariates of fisheries.
These covariates were fitted with 0-1 (t 0 and t −1 ) and 0-to 3-year time lags (t 0 , t −1 , t −2 and t −3 ) when testing their effect on measures of association and reproduction, respectively. The shorter (0-1 year) time lag was chosen as killer whale social structure at Marion Island is known to be fluid with observed differences between seasons (Jordaan et al., 2021). The longer (2-3 year) time lag was chosen for reproduction as this incorporates the 18-month gestation period, 12-month lactation period and the two-year minimum calving interval for killer whales Olesiuk et al., 2005;Tixier et al., 2015).

| Survival analysis
An encounter history matrix with 12 occasions was used to summarize individual identification data. Please see Jordaan et al., 2020 for details on data collection and processing as well as the age classes used. This study also showed similar sex-specific survival between males and females for these data, and therefore, this was not considered for this study (Jordaan et al., 2020).
Program MARK makes use of multiple encounters of animals with artificial or natural markings and, through maximum likelihood methods, estimates survival and other population parameters (e.g., probability of transition between states). We constructed multiple competing models and ranked these using Akaike's Information Criterion corrected for small sample sizes (AICc). The model with the lowest AICc value represents the best compromise between model fit and complexity, with differences in AICc values (ΔAICc) indicating relative model support (Burnham & Anderson, 2002).
Models received approximately equal support from the data if their ΔAICc scores were less than 2 units apart, though this is not a strict cutoff value (Burnham & Anderson, 2002). This approach of model selection assumes that the set of models included a general model that adequately fits the data. To verify this, goodness-offit testing was performed in U-CARE 2.2.2 (Choquet et al., 2009) to test whether the Jolly-MoVe (JMV) multistate model (Pradel et al., 2005) fitted the data. Homogeneous survival and detection probabilities among independently behaving marked animals are assumed in the JMV model (Pradel et al., 2005) in addition to the assumptions of capture-recapture models that marks are not lost, individuals are not misidentified, and sampling is instantaneous relative to the interval between occasions.
Using multistate capture-recapture models, we estimated survival (Φ), state transition (Ψ), and detection probabilities (p) of killer whales at Marion Island. We assumed time-dependent detection and state-dependent transition probabilities for all models, as these model structures were well supported in previous analysis of these data (Jordaan et al., 2020) and our interest was specifically on the survival parameter. For survival, we compared a model assuming constant survival (~1) to models with time-(~time), age class (~state), or covariate-dependent survival. Covariates ( Figure A1; Table A1) were added to test whether direct measures of prey availability (SES, FS, KP, and MP), measures of toothfish fishing effort (TF, TFc, TPHK, and TPHL), and indirect measures of prey availability (SOI, SAM, and SSTa) influenced survival between years. The significance of these covariates was evaluated using an analysis of deviance (ANODEV) test (Grosbois et al., 2008). Additionally, measures of social structure (DEGREE, HWI, and CC) were fitted as individual covariates to test whether survival probability varies as a function of social structure. All covariates (survival, social structure, and calving rate analyses) were standardized to mean = 0 and standard deviation = 1 to avoid numerical instabilities during analyses. Standardized covariates also allow for comparison of regression slopes between covariates that differ in order of magnitude (Schielzeth, 2010).

| Social structure and calving rate analysis
Population-level measures of association for weighted association networks were calculated in R 4.01 (R Core Team, 2020) with the "asnipe" (Farine, 2019) and "igraph" (Csardi & Nepusz, 2006) packages (see Jordaan et al., 2021). We calculated two measures of association between pairs of nodes (i.e., relationship measures): the mean distance between nodes (Mean distance) and centrality coefficient (CC; Figure A2a,b). Reproduction is represented by calving rate (the total number of calves born during a given year relative to the total number of "reproductively available" females in the population that year; Figure A2c; see Jordaan et al., 2020).
Linear mixed effects models with Gaussian error distributions (fitted using the "lme4" package (Bates et al., 2015) in R) were used to determine the relationship between response variables (Centrality, Mean distance, and calving rate) and covariates of interest. A set of models were constructed for each response variable, fitted with the "MuMIn" wrapper package (Bartoń, 2020) and ranked using the same AICc rules described previously. Covariates ( Figure A1; Table A1) were added individually to test whether direct measures of prey availability (SES, FS, KP, and MP), measures of toothfish fishing effort (TF, TFc, TPHK, and TPHL), and indirect measures of prey availability (SOI, SAM, and SSTa) influenced response variables between years. An ANODEV test (Grosbois et al., 2008) was used to evaluate the significance of these covariates. From these, a total of 52 killer whales were identified (after exclusions), with 16 calves born into the population during this period.

| RE SULTS
The encounter history data fitted the model assumptions according to goodness-of-fit test results, which showed nonsignificant results for component tests and the overall Jolly-MoVe (JMV) model (Table A2).

| Survival analysis
Multistate capture-recapture models with social structure indices as individual covariates were more parsimonious than those including prey, fishery, or environmental covariates. The model that included half-weight index (HWI; the proportion of time that two individuals spend together) in the same year as an individual covariate was most parsimonious (Table 1). According to this model, survival Still, models where survival was constrained as a linear function of TF (the number of hooks set) at t −1 and MP (macaroni penguin chick counts) at t 0 were 3.7 and 2.3 times better supported than the null model of constant survival (Table 1). These models showed that survival increased with the number of hooks set at t −1 ( Figure A3a) and when there were more macaroni penguin chicks counted at t 0 ( Figure A3b) and explained 50.34% and 40.35% of the observed variability in survival, respectively ( Table 2). The other covariates explained less of the variation in survival (their slope estimates were smaller and the 95% CI for β included zero; Table 2).

| Reproduction and social structure analyses
Mean distance (the mean distance between nodes in the sociality matrix) showed weak relationships with SSTa and KP at t −4 , but models incorporating these covariates were only marginally better (ΔAICc ranking) than the null model (Table 3), and ANODEV tests showed a nonsignificant effect on mean distance (Table A4).
When investigating centrality (CC), the best-supported model constrained centrality as a function of the Southern Oscillation Index 4 years previously (SOI at t −4 ) ( Table 3). In addition, six other covariates, all direct measures of prey availability at Marion Island (SES, FS and MP), had a significant effect on centrality during the current (t 0 ) and previous year (t −1 ) ( Table 4) (Table 5). Although other models reflecting various measures of prey availability and fisheries covariates also received some support, none of the covariates had a statistically significant effect on calving rate (Table A5).

| Fisheries and Marion Island killer whale survival
Prey availability (bottom-up regulation) is an important determinant of animal survival (Hunt Jr & McKinnell, 2006 (Pistorius et al., 2012;Reisinger et al., 2016;Reisinger, de Bruyn, Tosh, et al., 2011). It is possible that the population sizes of these prey species are large enough for killer whales to exhibit a Holling type II functional response (Holling, 1959), leading to no impact on survival. This functional response occurs when predation has reached a saturation plane within which decreases or increases in prey density will not change predation rates. A Holling type II response will also mask the reproductive benefits of increased prey availability. Note: Omitted 28 covariates (total of 38 covariates).Deviance F,df represents the deviance with the F-statistic and the number of degrees of freedom. Variation (%) refers to the percentage variation of the deviance that is explained by a covariate. All significant covariates (p < .05) are presented in bold text. Only covariates with variation >20% are presented (see Table A1 for full test results).

TA B L E 2 Analysis of deviance (ANODEV) test results showing the effect of covariates on the survival probability of killer whales at Marion Island (2006-2018).
Satellite-tracked killer whales that depart from Marion Island often move to seamounts north of the island, where they appear to forage on toothfish and possibly cephalopods (Reisinger et al., 2015(Reisinger et al., , 2016. These seamounts are also frequented by fisheries targeting toothfish, placing Marion Island killer whales in close proximity of fishing vessels . CCAMLR monitored toothfish fisheries in the area surrounding the archipelago lose 6% (15 tons) of their total annual catch to killer whale depredation  providing an artificial food source. We detected no effect of fishery effort on social structure. In contrast, survival of Marion Island killer whales showed a positive relationship with the number of hooks deployed by fishing vessels during the previous year (TF at t −1 ; Figure A3a). An increase in fishing effort therefore correlates with higher Marion Island killer whale survival 1 year later.

| Sociality, reproduction, and prey abundance
Prey abundance is also an important driver of sociality in predators. The costs associated with living in groups are outweighed by its benefits when prey availability, and therefore individual energy gain, is greater (reviewed in MacDonald & Johnson, 2015). Sociality is therefore fluid and can vary over time in response to changes in prey availability. The Note: Deviance F,df represents the deviance with the F-statistic and the number of degrees of freedom. Only significant covariates (p < .05) are presented and are presented in bold text (see Table A6 for full test results).

TA B L E 4 Analysis of deviance (ANODEV) test results
showing the effect of covariates on the centrality of killer whales at Marion Island.
Southern Resident killer whale population in the ENP, for example, is less social when their salmon prey availability is low and more social as prey availability increases (Foster et al., 2012). Here, we show that the link between prey availability and sociality is evident at Marion Island although the response of social structure is varied and dependent on prey type. This varied response in social structure is likely attributed to the generalist diet of this population (de Bruyn et al., 2013;Reisinger et al., 2015;Reisinger, de Bruyn, Tosh, et al., 2011). Previous work has shown that fission and fusion of killer whale social structure occur at Marion Island in response to seasonal changes in prey abundance (Jordaan et al., 2021). Sociality increases during periods of the year with greater prey abundance and decreases during winter, when prey is less abundant at the island (Jordaan et al., 2021).
We did not find relationships between prey availability and reproduction among Marion Island killer whales. Marion Island killer whales show reproduction rates closely resembling those of other global populations with differences among populations attributed to local ecology and stressors (Jordaan et al., 2020). Typically, resource availability is an important driver of reproduction in predator species with greater reproduction expected with increased prey availability through improvement of body condition (Brand & Keith, 1979).
Killer whales at Îles Crozet and in the Strait of Gibraltar demonstrate this trend and show greater reproductive outputs when exposed to increased prey availability associated with fisheries (Esteban et al., 2016;Tixier et al., 2015). Social benefits may be the reason for the absence of a relationship between prey availability and reproduction, as observed here for Marion Island killer whales.  Table A6).

TA B L E 5
Model selection results for the relationship between calving rate and covariates obtained from linear mixed effects models.  Table A7 for all models fitted).

| Limitations
Our analysis explored correlations between several response variables and a number of covariates, which increases the probability of a Type I error (i.e., that one or more of the covariates are significant due to chance; Gimenez & Barbraud, 2017). Gimenez and Barbraud (2017) suggest the use of a principal component analysis of covariates to resolve this issue. We performed a principal component analysis (results not shown) to reduce the number of covariates used in analysis, but none of the principal components were correlated with our response variables. Therefore, we decided to fit individual covariates in our models.
Another limitation is that we do not know how well our covariates reflect true prey availability to Marion Island killer whales and if the suit of covariates used covers all prey items of that this population feed on. These whales do not spend the entire year at the archipelago, and factors away from the island may thus also influence survival or social structure. For example, environmental proxies of prey abundance in the region of the seamounts did not correlate with killer whale survival, social structure, or reproduction. These environmental indices likely affect prey items at lower trophic levels, with the effects of these environmental factors taking time to reach apex predators like killer whales. However, the link between variability in climatic factors and foraging conditions and the impact these ultimately have on predators is not fully understood, particularly in the southern Indian Ocean Seyboth et al., 2016).

| CON CLUS ION
Artificial prey availability and social structure had the strongest correlation with the survival of Marion Island killer whales.
Natural, inshore, prey availability was not suggested to impact survival with annual fluctuations in prey abundances potentially buffered by changes in social structure. This finding further strengthens support for social structure as an important modulator of survival in social apex predators. Future increases in legal fishing activity may prove to be beneficial to some apex predator populations, but the effects of these on the ecosystem and potential resource competition between fisheries and predators are not known (Mul et al., 2020). Uncertainty remains as to how variable climatic factors ultimately influence apex predators, but understanding these relationships is vital given current and predicted changes in climate conditions (Bestley et al., 2020; Convey & Peck, 2019). writing -original draft (supporting); writing -review and editing (supporting). Nico de Bruyn: Conceptualization (equal); funding acquisition (lead); project administration (lead); resources (lead); supervision (equal); writing -review and editing (supporting).

CO N FLI C T O F I NTER E S T S TATEM ENT
The authors have no conflict of interest to declare.

F I G U R E A 5
Number of reproductively available females and number of calves born to killer whales at Marion Island from 2006 to 2018. Females were deemed to be reproductively available if they were over the age of 10 years and did not have 1-or 2-year-old calves.

F I G U R E A 4
Annual probability of detection of killer whales at Marion Island from 2006 to 2018. Error bars depicting lower and upper confidence limits of the 95% confidence interval are included.

F I G U R E A 3
Marion Island killer whale survival as a function of (a) the total number of toothfish hooks set the previous year (TF at t −1 ) and (b) the total number of breeding macaroni penguins in the current year (MP at t 0 ). The shaded area represents the 95% confidence interval. Point estimates for survival are from the most parsimonious fully time-dependent model. Error bars depict 95% confidence intervals.  (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) were analyzed to provide population-level social measures. The number of degrees of freedom (df), ΔAICc (the difference in AICc between the model with the lowest AICc value and the relevant model) and AICc weight (w i ) (the relative support of a model, in relation to the other models) and log likelihood (logLik) are presented.

TA B L E A 8
Full model selection results for mean distance, a populationlevel measure of social structure, and covariates obtained from linear mixed effects models.

TA B L E A 9
Full model selection results for centrality, a population-level measure of social structure, and covariates obtained from linear mixed effects models. Note: When significant (p < .05), p-values are presented in bold. R 2 is the coefficient of determination of the linear regression model.